No abstract

Readers do not always adopt the perspective of the protagonist; however, they will under certain conditions. Experiments 1a and 1b showed that readers will take the perspective of the protagonist from the third-person point of view, but only when explicitly instructed to do so. Experiment 2 demonstrated that reading from the first-person point of view is a text-based manipulation that encourages readers to adopt the perspective of the protagonist. The results of Experiments 3a and 3b replicated the findings of Experiments 1a and 2. Experiment 4 established that simply increasing readers' attention to the text does not lead to adoption of the protagonist's perspective; moreover, this suggests that when it does occur, protagonist perspective adoption is not the result of increased attention, but strategic processing.

As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students’ responses to a variety of assignments and tasks. While NLP is widely used to deliver students with formative feedback, it can also be used to provide educators with information about task difficulty, students’ individual differences, and student performance. In this chapter, we will first provide an overview of NLP, followed by a discussion of how NLP could be used to examine the learning process across a number of time points. Finally, we consider the future applications of NLP in the learning analytics domain.

Research in discourse processing has provided us with a strong foundation for understanding the characteristics of text and discourse, as well as their influence on our processing and representation of texts. However, recent advances in computational techniques have allowed researchers to examine discourse processes in new ways. The purpose of the current paper is to build on prior work in this domain and describe how new methodologies that consider the multi‐dimensional nature of texts can serve as a complement to the existing literature. We focus on natural language processing (NLP) methodologies, in which computers calculate information about the linguistic and semantic properties of language data. We first provide a context for the origins of computational discourse analysis through the integration of research across computer science and psychology. We then provide an overview of different NLP methodologies and describe prior work that has leveraged these techniques to advance theoretical perspectives of discourse comprehension and production. Finally, we propose new areas of research that integrate these advances with traditional research methodologies in the field.

Despite the centrality of the protagonist during narrative comprehension, evidence indicates that readers do not typically approach the text from the protagonist’s point of view. Experiments 1a–1c demonstrated that both explicit task instructions and the first-person point of view resulted in comprehension being influenced by perspective-relevant information; this indicated that readers were adopting the perspective of the protagonist. However, Experiments 2a–3b showed that even when readers adopt the protagonist’s perspective, they cannot do so to the exclusion of related perspective-irrelevant information. Results are discussed in the context of the RI-Val model of comprehension in which perspective-relevant information and perspective-irrelevant information are both available and compete for influence during comprehension.